Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-15T03:26:11.536Z Has data issue: false hasContentIssue false

A Study of Underwater Terrain Navigation based on the Robust Matching Method

Published online by Cambridge University Press:  13 February 2014

Kai Zhang*
Affiliation:
(Wuhan University, Wuhan, China)
Yong Li
Affiliation:
(University of New South Wales, Sydney, Australia)
Jianhu Zhao
Affiliation:
(Wuhan University, Wuhan, China)
Chris Rizos
Affiliation:
(University of New South Wales, Sydney, Australia)
*

Abstract

Outliers in terrain data are an obstacle to achieving accurate and robust solutions of Underwater Terrain Relative Navigation (UTRN). If not handled properly, navigation may be degraded or even divergent. To address the problem, this paper proposes a terrain-matching algorithm based on the robust estimation theory. In contrast to the conventional approach, the proposed algorithm can significantly reduce the interference of the outliers. Experimental results confirm the good performance of the proposed method.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Anderson, B.D. and Moore, J.B. (2012). Optimal filtering. DoverPublications. com.Google Scholar
Anonsen, K.B. (2010). Advances in Terrain Aided Navigation for Underwater Vehicles. Norwegian University of Science and Technology.Google Scholar
Anonsen, K.B. and Hallingstad, O. (2006). Terrain aided underwater navigation using point mass and particle filters. Proceedings of the IEEE/ION Position Location and Navigation Symposium.CrossRefGoogle Scholar
Bergman, N., Ljung, L. and Gustafsson, F. (1999). Terrain navigation using Bayesian statistics. IEEE Control Systems, 19 (3), 3340.Google Scholar
Donovan, G.T. (2012). Position Error Correction for an Autonomous Underwater Vehicle Inertial Navigation System (INS) Using a Particle Filter. IEEE Journal of Oceanic Engineering. 37 (3), 431445.CrossRefGoogle Scholar
Gandhi, M.A. and Mili, L. (2010). Robust Kalman filter based on a generalized maximum-likelihood-type estimator. IEEE Transactions on Signal Processing, 58 (5), 25092520.CrossRefGoogle Scholar
Gervini, D. and Yohai, V.J. (2002). A class of robust and fully efficient regression estimators. Annals of Statistics, 583616.Google Scholar
Golden, J.P. (1980). Terrain contour matching (TERCOM): a cruise missile guidance aid. Proceedings of the 24th Annual Technical Symposium, International Society for Optics and Photonics, 1018.Google Scholar
Gustafsson, F. (2010). Particle filter theory and practice with positioning applications. IEEE Aerospace and Electronic Systems Magazine, 25 (7), 5382.CrossRefGoogle Scholar
Fairfield, N. and Wettergreen, D. (2008). Active localization on the ocean floor with multibeam sonar, IEEE OCEANS, 110.CrossRefGoogle Scholar
Hampel, F.R., Ronchetti, E.M., Rousseeuw, P.J. and Stahel, W.A. (2011). Robust statistics: the approach based on influence functions. Wiley. com.Google Scholar
Hostetler, L. and Andreas, R. (1983). Nonlinear Kalman filtering techniques for terrain-aided navigation. IEEE Transactions on Automatic Control, 28 (3), 315323.CrossRefGoogle Scholar
Huber, P.J. (2011). Robust statistics. Springer.Google Scholar
Karlsson, R. and Gustafsson, F. (2003). Particle filter for underwater terrain navigation. IEEE Workshop on Statistical Signal Processing, 526529.CrossRefGoogle Scholar
Karlsson, R. and Gustafsson, F. (2006). Bayesian surface and underwater navigation. IEEE Transactions on Signal Processing, 54 (11), 42044213.CrossRefGoogle Scholar
Lin, Y., Yan, L., Liu, Y. and Tong, Q. (2008). Offline outlier identification for dynamic measurements in underwater geomagnetism navigation. 7th IEEE World Congress on Intelligent Control and Automation, 50095013.Google Scholar
Meduna, D.K. (2011). Terrain relative navigation for sensor-limited systems with application to underwater vehicles. Stanford University.Google Scholar
Meduna, D.K., Rock, S.M. and McEwen, R. (2008). Low-cost terrain relative navigation for long-range AUVs. IEEE OCEANS, 17.CrossRefGoogle Scholar
Meduna, D.K., Rock, S.M. and McEwen, R.S. (2010). Closed-loop terrain relative navigation for AUVs with non-inertial grade navigation sensors. IEEE Autonomous Underwater Vehicles (AUV), IEEE/OES., 18.CrossRefGoogle Scholar
Nygren, I. and Jansson, M. (2004). Terrain navigation for underwater vehicles using the correlator method. IEEE Journal of Oceanic Engineering, 29 (3), 906915.CrossRefGoogle Scholar
Rousseeuw, P.J. (1984). Least median of squares regression. Journal of the American statistical association, 79 (388), 871880.CrossRefGoogle Scholar
Runnalls, A.R., Groves, P.D. and Handley, R.J. (2005). Terrain-referenced navigation using the IGMAP data fusion algorithm. Proceedings of the 61st Annual Meeting of the Institute of Navigation, 976986.Google Scholar